The present invention relates generally to Orthogonal Frequency Division Multiplexing (OFDM) based data communication, and, more specifically, to a method and system for determining a symbol boundary in a Multiple-Input-Multiple-Output (MIMO) OFDM data communication system.
OFDM uses a digital multi-carrier scheme that has one or more orthogonal sub-carriers. Orthogonality of the sub-carriers is achieved by spacing the sub-carriers with a minimum frequency and thus, preventing them from interfering with each other. Each sub-carrier communicates data in the form a stream of data packets. Further, each sub-carrier is modulated using a conventional modulation technique (e.g., quadrature amplitude modulation or phase shift keying) at a low symbol rate. The use of a low symbol rate leads to improved tolerance to multipath delay. Due to the use of multiple sub-carriers, the total data rate is equivalent to conventional single-carrier modulation techniques.
The increased tolerance to multipath delays without any impact on the total data rate has increased the popularity of OFDM communication systems. OFDM can be used for both wired and wireless data communication. In wireless communication systems, it has been incorporated in various IEEE standards such as 802.11, 802.16.
In accordance with standards such as IEEE 802.11a for Wireless Local Area Network (WLAN) system, an OFDM data packet has a standard field structure. The field structure of a typical OFDM burst packet will be explained below in conjunction with FIGS. 1A and 1B.
FIGS. 1A and 1B illustrate a data packet 100 in accordance with IEEE 801.11a WLAN standard. Referring to FIG. 1A, the data packet 100 includes a short training field (STF) 102, a long training field (LTF) 104, a signal (SIG) field 106, and a rest of packet (ROP) field 108. FIG. 1B depicts detailed views of the STF 102 and the LTF 104.
The STF 102 includes a set of ten (10) identical short preambles S1, S2, etc., each having a duration of 0.8 μs and in which each short preamble includes 16 samples. Thus, the STF 102 includes 160 samples. The STF 102 is followed by the LTF 104. The LTF 104 includes a guard interval (GI2) and two identical long preambles LP1 and LP2. The time duration of GI2 is 1.6 μs and includes 32 samples. The GI2 is followed by the two identical long preambles LP1 and LP2, each having time duration of 3.2 μs, and each long preamble includes 64 samples. The 32 samples of GI2 are a copy of the last 32 samples of LP1 or LP2. The LTF 104 is followed by the SIG field 106, which includes 160 samples representing information relating to the ROP field 108. The ROP field 108 includes data represented as symbols in which each symbol includes 64 samples. Further, each symbol is preceded by a guard interval of 16 samples that are a copy of the last 16 samples of the corresponding symbol. This technique helps to reduce Inter Symbol Interference (ISI). For extracting data from the data packet 100, the correct boundary of OFDM symbols must be known. The detection of the boundary is known as Symbol Boundary Detection (SBD). The symbol boundary is found using the known information of the STF and the LTF of the received packet. Traditionally, two methodologies have been used to find the symbol boundary. The first method finds the boundary between S10 and GI2 (STF-GI2), and the second one finds the boundary between GI2 and LP1 (GI2-LTF), as in FIG. 1B.
Existing OFDM systems use various synchronization schemes such as auto-correlation and cross-correlation to perform SBD. The auto-correlation scheme entails calculation of auto-correlation values for the samples obtained subsequent to sampling of a received signal with previously obtained samples of the received signals. The auto correlation scheme exploits the periodic nature of the symbols in the STF, where a sample belonging to one preamble is repeated at the corresponding sample position in the subsequently received preambles. The auto-correlation values rise as the receiver starts receiving the short preambles. Thereafter, the auto-correlation values become stable for a time duration during which the STF is received, thereby forming a plateau. A fall in the auto-correlation values marks the end of the STF and the beginning of the GI2. The sample corresponding to the fall in the auto-correlation values is recorded and used as an estimate for the STF-GI2 boundary. However, due to high noise and low Signal to Noise Ratios (SNRs), the sampling instant corresponding to the above-mentioned fall may be recorded at a position that is offset from a correct boundary due to the poor correlation metric of short preambles at low SNR. Thus, the timing variance of the auto-correlation synchronization scheme is large and may degrade performance of the OFDM system. Accordingly, such auto-correlation synchronization schemes are used to achieve only a coarse estimate of the STF-GI2 boundary.
To overcome this shortcoming of the auto-correlation synchronization scheme, cross-correlation synchronization may be used. Cross-correlation entails calculation of cross-correlation values of known LTF samples with the samples in the vicinity of the GI2-LTF boundary that was estimated based on the coarse STF-GI2 boundary estimate. The peak of the cross-correlation output is detected to determine an estimate of the GI2-LTF boundary. Such a cross-correlation scheme is used to achieve a fine boundary estimate because it has a better correlation metric as well as a sharp gradient. Although, the combination of auto-correlation and cross-correlation provides improved signal boundary estimates as compared to the method using only auto-correlation, the combination performs poorly at low SNR. The poor performance may be attributed to the poor cross-correlation metric of the LTF and poor auto-correlation metric of the STF. As a result considerable errors may be introduced in the SBD process.
In order to overcome these shortcomings, inverted half auto-correlation is used for fine-boundary estimation instead of cross-correlation. The fine-boundary estimation is followed by processing of the fine-boundary estimate in a correction module. The correction module entails calculation of a predetermined number, such as three, of auto-correlation values (ACVs) corresponding to a second set of samples obtained subsequent to the fine-symbol boundary estimate. Each of the ACVs is obtained corresponding to a set of 64 samples, which are separated by 32 samples. These ACVs are subsequently compared with a maximum ACV value that is obtained during the coarse boundary estimation. A shift is provided to the fine-boundary estimate in the direction of the maximum ACV value to obtain an accurate signal boundary estimate.
However, the above solution fails in systems that have pseudo-multipath problems. Such systems have multiple copies of a signal, each of which has a varying rotation in the time domain for each symbol. As a result, the training fields from one antenna appear as time-shifted versions of the trainings fields from another antenna. Although such a scheme effectively avoids unintentional beam forming, it does not overcome the pseudo-multipath problem, and the pseudo-multipath problem leads to erroneous symbol boundary detection.
FIG. 2A illustrates the data packet 100 of FIGS. 1A and 1B and a time-domain rotated data packet 200. FIG. 2B is a graph illustrating the signal strength (RSSI) of an ideal boundary 202 and a delayed boundary 204. The data packet 100, in addition to elements illustrated in FIGS. 1A and 1B, shows the LTF 104 in detail. The LTF 104 includes a GI2, LP1, and LP2. The samples of the time-domain rotated data packet 200 have been given a cyclic rotation of two (2) samples separately in GI2, LP1, and LP2. As a result, the time-domain rotated data packet 200 appears as a time-shifted version of the data packet 100. Apart from cyclic rotation in the time-domain, the time-domain rotated data packet 200 is identical to the data packet 100. The data packet 100 is provided the time domain cyclic rotation and then relayed from a different antenna to avoid unintentional beam forming. However, the difference in the RSSIs of the carrier signal carrying the data packet 100 and of the carrier signal carrying the time-domain rotated data packet 200, leads to erroneous symbol boundary detection. Thus, instead of detecting the ideal boundary 202 as the symbol boundary, the delayed boundary 204 is detected as the symbol boundary, which causes a synchronization error.
It would be advantageous to be able to accurately and correctly detect a symbol boundary in an OFDM-MIMO system.